如何使用具有面部特征的openCV训练支持向量机(svm)分类器？

opencv中的机器学习算法都带有类似的界面.为了训练它,你传递一个NxM Mat offeatures(N行,每个特征一行长度为M)和一个带有类标签的Nx1 Mat.像这样:

//traindata      //trainlabels

f e a t u r e    1 
f e a t u r e    -1
f e a t u r e    1
f e a t u r e    1
f e a t u r e    -1

对于预测,您以相同的方式填充1行的Mat,它将返回预测的标签

所以,比方说,你的16个面部点存储在一个矢量中,你会这样做:

Mat trainData; // start empty
Mat labels;

for all facial_point_vecs:
{
    for( size_t i=0; i<16; i++ )
    {
        trainData.push_back(point[i]);
    }
    labels.push_back(label); // 1 or -1
}
// now here comes the magic:
// reshape it, so it has N rows, each being a flat float, x,y,x,y,x,y,x,y... 32 element array
trainData = trainData.reshape(1, 16*2); // numpoints*2 for x,y

// we have to convert to float:
trainData.convertTo(trainData,CV_32F);

SVM svm; // params omitted for simplicity (but that's where the *real* work starts..)
svm.train( trainData, labels );


//later predict:
vector<Point> points;
Mat testData = Mat(points).reshape(1,32); // flattened to 1 row
testData.convertTo(testData ,CV_32F);
float p = svm.predict( testData );